





"""simple docstring"""





import argparse
import json

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import ConvNextConfig, SegformerImageProcessor, UperNetConfig, UperNetForSemanticSegmentation


def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		Optional[Any]     ):







      '''simple docstring'''




      lowerCAmelCase    		=			3_84
      if "tiny" in model_name:
            lowerCAmelCase    		=			[3, 3, 9, 3]
            lowerCAmelCase    		=			[96, 1_92, 3_84, 7_68]
      if "small" in model_name:
            lowerCAmelCase    		=			[3, 3, 27, 3]
            lowerCAmelCase    		=			[96, 1_92, 3_84, 7_68]
      if "base" in model_name:
            lowerCAmelCase    		=			[3, 3, 27, 3]
            lowerCAmelCase    		=			[1_28, 2_56, 5_12, 10_24]
            lowerCAmelCase    		=			5_12
      if "large" in model_name:
            lowerCAmelCase    		=			[3, 3, 27, 3]
            lowerCAmelCase    		=			[1_92, 3_84, 7_68, 15_36]
            lowerCAmelCase    		=			7_68
      if "xlarge" in model_name:
            lowerCAmelCase    		=			[3, 3, 27, 3]
            lowerCAmelCase    		=			[2_56, 5_12, 10_24, 20_48]
            lowerCAmelCase    		=			10_24

      # set label information
      lowerCAmelCase    		=			1_50
      lowerCAmelCase    		=			"""huggingface/label-files"""
      lowerCAmelCase    		=			"""ade20k-id2label.json"""
      lowerCAmelCase    		=			json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE				,		repo_type="""dataset"""     )				,		"""r"""     )     )
      lowerCAmelCase    		=			{int(SCREAMING_SNAKE_CASE     ): v for k, v in idalabel.items()}
      lowerCAmelCase    		=			{v: k for k, v in idalabel.items()}

      lowerCAmelCase    		=			ConvNextConfig(
          depths=SCREAMING_SNAKE_CASE				,		hidden_sizes=SCREAMING_SNAKE_CASE				,		out_features=["""stage1""", """stage2""", """stage3""", """stage4"""]     )
      lowerCAmelCase    		=			UperNetConfig(
          backbone_config=SCREAMING_SNAKE_CASE				,		auxiliary_in_channels=SCREAMING_SNAKE_CASE				,		num_labels=SCREAMING_SNAKE_CASE				,		idalabel=SCREAMING_SNAKE_CASE				,		labelaid=SCREAMING_SNAKE_CASE				,		)

      return config


def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		int     ):







      '''simple docstring'''




      lowerCAmelCase    		=			[]

      # fmt: off
      # stem
      rename_keys.append(("""backbone.downsample_layers.0.0.weight""", """backbone.embeddings.patch_embeddings.weight""")     )
      rename_keys.append(("""backbone.downsample_layers.0.0.bias""", """backbone.embeddings.patch_embeddings.bias""")     )
      rename_keys.append(("""backbone.downsample_layers.0.1.weight""", """backbone.embeddings.layernorm.weight""")     )
      rename_keys.append(("""backbone.downsample_layers.0.1.bias""", """backbone.embeddings.layernorm.bias""")     )
      # stages
      for i in range(len(config.backbone_config.depths     )     ):
            for j in range(config.backbone_config.depths[i]     ):
                  rename_keys.append((F'backbone.stages.{i}.{j}.gamma', F'backbone.encoder.stages.{i}.layers.{j}.layer_scale_parameter')     )
                  rename_keys.append((F'backbone.stages.{i}.{j}.depthwise_conv.weight', F'backbone.encoder.stages.{i}.layers.{j}.dwconv.weight')     )
                  rename_keys.append((F'backbone.stages.{i}.{j}.depthwise_conv.bias', F'backbone.encoder.stages.{i}.layers.{j}.dwconv.bias')     )
                  rename_keys.append((F'backbone.stages.{i}.{j}.norm.weight', F'backbone.encoder.stages.{i}.layers.{j}.layernorm.weight')     )
                  rename_keys.append((F'backbone.stages.{i}.{j}.norm.bias', F'backbone.encoder.stages.{i}.layers.{j}.layernorm.bias')     )
                  rename_keys.append((F'backbone.stages.{i}.{j}.pointwise_conv1.weight', F'backbone.encoder.stages.{i}.layers.{j}.pwconv1.weight')     )
                  rename_keys.append((F'backbone.stages.{i}.{j}.pointwise_conv1.bias', F'backbone.encoder.stages.{i}.layers.{j}.pwconv1.bias')     )
                  rename_keys.append((F'backbone.stages.{i}.{j}.pointwise_conv2.weight', F'backbone.encoder.stages.{i}.layers.{j}.pwconv2.weight')     )
                  rename_keys.append((F'backbone.stages.{i}.{j}.pointwise_conv2.bias', F'backbone.encoder.stages.{i}.layers.{j}.pwconv2.bias')     )
            if i > 0:
                  rename_keys.append((F'backbone.downsample_layers.{i}.0.weight', F'backbone.encoder.stages.{i}.downsampling_layer.0.weight')     )
                  rename_keys.append((F'backbone.downsample_layers.{i}.0.bias', F'backbone.encoder.stages.{i}.downsampling_layer.0.bias')     )
                  rename_keys.append((F'backbone.downsample_layers.{i}.1.weight', F'backbone.encoder.stages.{i}.downsampling_layer.1.weight')     )
                  rename_keys.append((F'backbone.downsample_layers.{i}.1.bias', F'backbone.encoder.stages.{i}.downsampling_layer.1.bias')     )

            rename_keys.append((F'backbone.norm{i}.weight', F'backbone.hidden_states_norms.stage{i+1}.weight')     )
            rename_keys.append((F'backbone.norm{i}.bias', F'backbone.hidden_states_norms.stage{i+1}.bias')     )

      # decode head
      rename_keys.extend(
          [
              ("""decode_head.conv_seg.weight""", """decode_head.classifier.weight"""),
              ("""decode_head.conv_seg.bias""", """decode_head.classifier.bias"""),
              ("""auxiliary_head.conv_seg.weight""", """auxiliary_head.classifier.weight"""),
              ("""auxiliary_head.conv_seg.bias""", """auxiliary_head.classifier.bias"""),
          ]     )
      # fmt: on

      return rename_keys


def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		Any				,		SCREAMING_SNAKE_CASE	:		Dict				,		SCREAMING_SNAKE_CASE	:		str     ):







      '''simple docstring'''




      lowerCAmelCase    		=			dct.pop(SCREAMING_SNAKE_CASE     )
      lowerCAmelCase    		=			val


def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		str				,		SCREAMING_SNAKE_CASE	:		List[str]				,		SCREAMING_SNAKE_CASE	:		Union[str, Any]     ):







      '''simple docstring'''




      lowerCAmelCase    		=			{
          """upernet-convnext-tiny""": """https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_tiny_fp16_512x512_160k_ade20k/upernet_convnext_tiny_fp16_512x512_160k_ade20k_20220227_124553-cad485de.pth""",
          """upernet-convnext-small""": """https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_small_fp16_512x512_160k_ade20k/upernet_convnext_small_fp16_512x512_160k_ade20k_20220227_131208-1b1e394f.pth""",
          """upernet-convnext-base""": """https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_base_fp16_512x512_160k_ade20k/upernet_convnext_base_fp16_512x512_160k_ade20k_20220227_181227-02a24fc6.pth""",
          """upernet-convnext-large""": """https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_large_fp16_640x640_160k_ade20k/upernet_convnext_large_fp16_640x640_160k_ade20k_20220226_040532-e57aa54d.pth""",
          """upernet-convnext-xlarge""": """https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_xlarge_fp16_640x640_160k_ade20k/upernet_convnext_xlarge_fp16_640x640_160k_ade20k_20220226_080344-95fc38c2.pth""",
      }
      lowerCAmelCase    		=			model_name_to_url[model_name]
      lowerCAmelCase    		=			torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE				,		map_location="""cpu"""     )["""state_dict"""]

      lowerCAmelCase    		=			get_upernet_config(SCREAMING_SNAKE_CASE     )
      lowerCAmelCase    		=			UperNetForSemanticSegmentation(SCREAMING_SNAKE_CASE     )
      model.eval()

      # replace "bn" => "batch_norm"
      for key in state_dict.copy().keys():
            lowerCAmelCase    		=			state_dict.pop(SCREAMING_SNAKE_CASE     )
            if "bn" in key:
                  lowerCAmelCase    		=			key.replace("""bn"""				,		"""batch_norm"""     )
            lowerCAmelCase    		=			val

      # rename keys
      lowerCAmelCase    		=			create_rename_keys(SCREAMING_SNAKE_CASE     )
      for src, dest in rename_keys:
            rename_key(SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE     )

      model.load_state_dict(SCREAMING_SNAKE_CASE     )

      # verify on image
      lowerCAmelCase    		=			"""https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg"""
      lowerCAmelCase    		=			Image.open(requests.get(SCREAMING_SNAKE_CASE				,		stream=SCREAMING_SNAKE_CASE     ).raw     ).convert("""RGB"""     )

      lowerCAmelCase    		=			SegformerImageProcessor()
      lowerCAmelCase    		=			processor(SCREAMING_SNAKE_CASE				,		return_tensors="""pt"""     ).pixel_values

      with torch.no_grad():
            lowerCAmelCase    		=			model(SCREAMING_SNAKE_CASE     )

      if model_name == "upernet-convnext-tiny":
            lowerCAmelCase    		=			torch.tensor(
                [[-8.81_10, -8.81_10, -8.65_21], [-8.81_10, -8.81_10, -8.65_21], [-8.77_46, -8.77_46, -8.61_30]]     )
      elif model_name == "upernet-convnext-small":
            lowerCAmelCase    		=			torch.tensor(
                [[-8.82_36, -8.82_36, -8.67_71], [-8.82_36, -8.82_36, -8.67_71], [-8.76_38, -8.76_38, -8.62_40]]     )
      elif model_name == "upernet-convnext-base":
            lowerCAmelCase    		=			torch.tensor(
                [[-8.85_58, -8.85_58, -8.69_05], [-8.85_58, -8.85_58, -8.69_05], [-8.76_69, -8.76_69, -8.60_21]]     )
      elif model_name == "upernet-convnext-large":
            lowerCAmelCase    		=			torch.tensor(
                [[-8.66_60, -8.66_60, -8.62_10], [-8.66_60, -8.66_60, -8.62_10], [-8.63_10, -8.63_10, -8.59_64]]     )
      elif model_name == "upernet-convnext-xlarge":
            lowerCAmelCase    		=			torch.tensor(
                [[-8.49_80, -8.49_80, -8.39_77], [-8.49_80, -8.49_80, -8.39_77], [-8.43_79, -8.43_79, -8.34_12]]     )
      print("""Logits:"""				,		outputs.logits[0, 0, :3, :3]     )
      assert torch.allclose(outputs.logits[0, 0, :3, :3]				,		SCREAMING_SNAKE_CASE				,		atol=1e-4     )
      print("""Looks ok!"""     )

      if pytorch_dump_folder_path is not None:
            print(F'Saving model {model_name} to {pytorch_dump_folder_path}'     )
            model.save_pretrained(SCREAMING_SNAKE_CASE     )
            print(F'Saving processor to {pytorch_dump_folder_path}'     )
            processor.save_pretrained(SCREAMING_SNAKE_CASE     )

      if push_to_hub:
            print(F'Pushing model and processor for {model_name} to hub'     )
            model.push_to_hub(F'openmmlab/{model_name}'     )
            processor.push_to_hub(F'openmmlab/{model_name}'     )


if __name__ == "__main__":
       SCREAMING_SNAKE_CASE__   =  argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           "--model_name",
           default="upernet-convnext-tiny",
           type=str,
           choices=[f'upernet-convnext-{size}' for size in ["tiny", "small", "base", "large", "xlarge"]],
           help="Name of the ConvNext UperNet model you'd like to convert.",
       )
       parser.add_argument(
           "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
       )
       parser.add_argument(
           "--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
       )

       SCREAMING_SNAKE_CASE__   =  parser.parse_args()
       convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)













"""simple docstring"""





class 						lowercase   :





      def __init__(			self					)						->	Any:
            lowerCAmelCase    		=			""""""
            lowerCAmelCase    		=			""""""
            lowerCAmelCase    		=			[]





      def        _snake_case     (			self ,		lowercase ,		lowercase					)						->	int:
            if m == -1:
                  return n + 1
            elif n == -1:
                  return m + 1
            elif self.dp[m][n] > -1:
                  return self.dp[m][n]
            else:
                  if self.worda[m] == self.worda[n]:
                        lowerCAmelCase    		=			self.__min_dist_top_down_dp(m - 1 ,		n - 1					)
                  else:
                        lowerCAmelCase    		=			self.__min_dist_top_down_dp(lowercase ,		n - 1					)
                        lowerCAmelCase    		=			self.__min_dist_top_down_dp(m - 1 ,		lowercase					)
                        lowerCAmelCase    		=			self.__min_dist_top_down_dp(m - 1 ,		n - 1					)
                        lowerCAmelCase    		=			1 + min(lowercase ,		lowercase ,		lowercase					)

                  return self.dp[m][n]





      def        _snake_case     (			self ,		lowercase ,		lowercase					)						->	int:
            lowerCAmelCase    		=			worda
            lowerCAmelCase    		=			worda
            lowerCAmelCase    		=			[[-1 for _ in range(len(lowercase					)					)] for _ in range(len(lowercase					)					)]

            return self.__min_dist_top_down_dp(len(lowercase					) - 1 ,		len(lowercase					) - 1					)





      def        _snake_case     (			self ,		lowercase ,		lowercase					)						->	int:
            lowerCAmelCase    		=			worda
            lowerCAmelCase    		=			worda
            lowerCAmelCase    		=			len(lowercase					)
            lowerCAmelCase    		=			len(lowercase					)
            lowerCAmelCase    		=			[[0 for _ in range(n + 1					)] for _ in range(m + 1					)]

            for i in range(m + 1					):
                  for j in range(n + 1					):
                        if i == 0:  # first string is empty
                              lowerCAmelCase    		=			j
                        elif j == 0:  # second string is empty
                              lowerCAmelCase    		=			i
                        elif worda[i - 1] == worda[j - 1]:  # last characters are equal
                              lowerCAmelCase    		=			self.dp[i - 1][j - 1]
                        else:
                              lowerCAmelCase    		=			self.dp[i][j - 1]
                              lowerCAmelCase    		=			self.dp[i - 1][j]
                              lowerCAmelCase    		=			self.dp[i - 1][j - 1]
                              lowerCAmelCase    		=			1 + min(lowercase ,		lowercase ,		lowercase					)
            return self.dp[m][n]


if __name__ == "__main__":
       SCREAMING_SNAKE_CASE__   =  EditDistance()

       print("****************** Testing Edit Distance DP Algorithm ******************")
       print()

       SCREAMING_SNAKE_CASE__   =  input("Enter the first string: ").strip()
       SCREAMING_SNAKE_CASE__   =  input("Enter the second string: ").strip()

       print()
       print(f'The minimum edit distance is: {solver.min_dist_top_down(Sa, Sa)}')
       print(f'The minimum edit distance is: {solver.min_dist_bottom_up(Sa, Sa)}')
       print()
       print("*************** End of Testing Edit Distance DP Algorithm ***************")






